A Survey Of Different Workflow Scheduling Algorithms In Cloud Computing

Seethalakshmi, V.; Govindasamy, V.; Akila, V.; Sivaranjini, G.; Sindhuja, K.; Prasanth, K.

doi:10.1109/iccpeic45300.2019.9082400

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Gao et al 26 presented implicit knowledge in the Industrial Internet of Things (IIoT) utilizing collaborative learning strategies. They also investigated the possible links between users and APIs in consideration of the expanded dimensions and dynamics of Internet of Things (IoT) devices, thereby improving the matrix factorization model by using mined implicit knowledge about both sides' implicit links.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Reference 22 increases the waiting time and References 23,29 security during task scheduling is not considered. Reference 24 needs further improvement in cloud computing in resource management and security is not considered in References 25‐27. In Reference 28, workload prediction is not determined, and Reference 31 needs improvement and requires the consideration of new clustering algorithms and factorization models.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Confinement forest‐based enhanced min‐min and max‐min technique for secure multicloud task scheduling

Jawade

Borkar²,

Ramachandram

2022

Trans Emerging Tel Tech

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Task scheduling is the most important issue in the cloud computing environment since its implementation is largely dependent on it. However, due to its weaknesses in energy consumption for resource usage, high computational complexity, and memory needs, Virtual Machine (VM) cannot manage large amounts of workload. Furthermore, by reducing the number of active hosts' data assignments while utilizing the resources, a security risk occurs. To address these challenges, a Grey-wolf with Deadline Restrictions approach was developed to allocate tasks and resources in less time while maintaining fixed interval constraints. Moreover, the Cognizing Anomaly detection method is utilized to find even the most recent abnormalities in the data. Furthermore, using the Confinement Forest-based augmented Min-Min and Max-Min approach, the task scheduling process provides an efficient workload balancing mechanism by restricting makespan, energy consumption, load balancing, and fault tolerance. In contrast, enabling a security-aware multicharacteristic-based heuristic technique is employed to provide secure placement of data in the appropriate location. The proposed technique outperforms existing methods in terms of makespan and memory consumption.

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Section: Literature Reviewmentioning

confidence: 99%